Most marketing AI initiatives stall because they're handed down from leadership rather than built around the workflow problems analysts and strategists face every day.

This session walks through a bottom-up adoption model and the PULSE framework for evaluating, building, and deploying AI agents that get used, using a real-world Marketing Analyst Agent (built to unify inbound performance data across paid social, paid search, and organic channels) as the case study. You'll leave with a repeatable approach for identifying the right AI opportunities in your organization and the concrete metrics that prove ROI to your CMO and executive leadership.

After this session, you’ll be able to:

•  Apply the PULSE framework to evaluate which marketing workflows are genuinely ready for an AI agent versus which should stay human-led, so you stop wasting cycles on use cases that won't ship.
• Replicate a bottom-up adoption model that surfaces high-value AI opportunities from the practitioners closest to the work, instead of forcing top-down mandates that quietly die in pilot.
• Identify the data sources, integrations, and guardrails required to build a Marketing Analyst Agent that produces cross-channel insights your team and leadership actually trust.
• Use a concrete metrics framework (i.e., time saved per report, speed-to-insight, decisions accelerated, adoption rate) to prove AI agent ROI in language your CMO will sign off on.